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A systematic study of multi-level query understanding
Li, Yanen
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https://hdl.handle.net/2142/50592
Description
- Title
- A systematic study of multi-level query understanding
- Author(s)
- Li, Yanen
- Issue Date
- 2014-09-16
- Director of Research (if dissertation) or Advisor (if thesis)
- Zhai, ChengXiang
- Doctoral Committee Chair(s)
- Zhai, ChengXiang
- Committee Member(s)
- Han, Jiawei
- Schatz, Bruce R.
- Roth, Dan
- Hsu, Bo-June
- Department of Study
- Computer Science
- Discipline
- Computer Science
- Degree Granting Institution
- University of Illinois at Urbana-Champaign
- Degree Name
- Ph.D.
- Degree Level
- Dissertation
- Keyword(s)
- Web Search
- Query Understanding
- Multi-Level Query Understanding
- Query Spelling Correction
- Query Segmentation
- Query Semantics
- Query Auto-Completion
- Abstract
- Search and information retrieval technologies have significantly transformed the way people seek information and acquire knowledge from the internet. To further improve the search accuracy and usability of the current-generation search engines, one of the most important research challenges is for a search engine to accurately understand a user’s intent or information need underlying the query. This thesis presents a systematic study of query understanding. In this thesis I have proposed a conceptual framework where there are different levels of query understanding. And these levels of query understanding have natural logical dependency. After that, I will present my studies on addressing important research questions in this framework. First, as a major type of query alteration, I addressed the query spelling correction problem by modeling all major types of spelling errors with a generalized Hidden Markov Model. Second, query segmentation is the most important type of query linguistic signals. I proposed a probabilistic model to identify the query segmentations using clickthrough data. Third, synonym finding is an important challenge for semantic annotation of queries. I proposed a compact clustering framework to mine entity attribute synonyms for a set of inputs jointly with multiple information sources. And finally, in the dynamic query understanding, I introduced the horizontal skipping bias which is unique to the query auto- completion process (QAC). I then proposed a novel two-dimensional click model for modeling the QAC process with emphasis on such behavior.
- Graduation Semester
- 2014-08
- Permalink
- http://hdl.handle.net/2142/50592
- Copyright and License Information
- Copyright 2014 Yanen Li
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Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at IllinoisDissertations and Theses - Computer Science
Dissertations and Theses from the Dept. of Computer ScienceManage Files
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